Merge pull request #1368 from WenmuZhou/dygraph_rc

[Dygraph] add config file of DB, CRNN,Rosetta, StarNet

configs/det/det_mv3_db.yml

@@ -45,9 +45,7 @@ Optimizer:
   beta1: 0.9
   beta2: 0.999
   lr:
-#    name: Cosine
     learning_rate: 0.001
-#    warmup_epoch: 0
   regularizer:
     name: 'L2'
     factor: 0

configs/det/det_r50_vd_db.yml

+Global:
+  use_gpu: true
+  epoch_num: 1200
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/det_rc/det_r50_vd/
+  save_epoch_step: 1200
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [5000,4000]
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  load_static_weights: True
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_db/predicts_db.txt
+
+Architecture:
+  model_type: det
+  algorithm: DB
+  Transform:
+  Backbone:
+    name: ResNet
+    layers: 50
+  Neck:
+    name: DBFPN
+    out_channels: 256
+  Head:
+    name: DBHead
+    k: 50
+
+Loss:
+  name: DBLoss
+  balance_loss: true
+  main_loss_type: DiceLoss
+  alpha: 5
+  beta: 10
+  ohem_ratio: 3
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+PostProcess:
+  name: DBPostProcess
+  thresh: 0.3
+  box_thresh: 0.7
+  max_candidates: 1000
+  unclip_ratio: 1.5
+
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [0.5]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
+            - { 'type': Resize, 'args': { 'size': [0.5, 3] } }
+      - EastRandomCropData:
+          size: [640, 640]
+          max_tries: 50
+          keep_ratio: true
+      - MakeBorderMap:
+          shrink_ratio: 0.4
+          thresh_min: 0.3
+          thresh_max: 0.7
+      - MakeShrinkMap:
+          shrink_ratio: 0.4
+          min_text_size: 8
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 16
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+          image_shape: [736, 1280]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 8
\ No newline at end of file

configs/rec/rec_mv3_none_bilstm_ctc.yml

@@ -5,7 +5,7 @@ Global:
   print_batch_step: 10
   save_model_dir: ./output/rec/mv3_none_bilstm_ctc/
   save_epoch_step: 3
-  # evaluation is run every 5000 iterations after the 4000th iteration
+  # evaluation is run every 2000 iterations
   eval_batch_step: [0, 2000]
   # if pretrained_model is saved in static mode, load_static_weights must set to True
   cal_metric_during_train: True
@@ -13,7 +13,7 @@ Global:
   checkpoints:
   save_inference_dir:
   use_visualdl: False
-  infer_img: doc/imgs_words/ch/word_1.jpg
+  infer_img: doc/imgs_words_en/word_10.png
   # for data or label process
   character_dict_path: 
   character_type: en
@@ -21,7 +21,6 @@ Global:
   infer_mode: False
   use_space_char: False
 
-
 Optimizer:
   name: Adam
   beta1: 0.9

configs/rec/rec_mv3_none_none_ctc.yml

+Global:
+  use_gpu: True
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/mv3_none_none_ctc/
+  save_epoch_step: 3
+  # evaluation is run every 2000 iterations
+  eval_batch_step: [0, 2000]
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path:
+  character_type: en
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0005
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+Architecture:
+  model_type: rec
+  algorithm: Rosetta
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+  Neck:
+    name: SequenceEncoder
+    encoder_type: reshape
+  Head:
+    name: CTCHead
+    fc_decay: 0.0004
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: LMDBDateSet
+    data_dir: ./train_data/data_lmdb_release/training/
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: LMDBDateSet
+    data_dir: ./train_data/data_lmdb_release/validation/
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/rec_mv3_tps_bilstm_ctc.yml

+Global:
+  use_gpu: true
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/mv3_tps_bilstm_ctc/
+  save_epoch_step: 3
+  # evaluation is run every 2000 iterations
+  eval_batch_step: [0, 2000]
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path:
+  character_type: en
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0005
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+Architecture:
+  model_type: rec
+  algorithm: STARNet
+  Transform:
+    name: TPS
+    num_fiducial: 20
+    loc_lr: 0.1
+    model_name: small
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 96
+  Head:
+    name: CTCHead
+    fc_decay: 0.0004
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: LMDBDateSet
+    data_dir: ./train_data/data_lmdb_release/training/
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: LMDBDateSet
+    data_dir: ./train_data/data_lmdb_release/validation/
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 4

configs/rec/rec_r34_vd_none_bilstm_ctc.yml

@@ -5,7 +5,7 @@ Global:
   print_batch_step: 10
   save_model_dir: ./output/rec/r34_vd_none_bilstm_ctc/
   save_epoch_step: 3
-  # evaluation is run every 5000 iterations after the 4000th iteration
+  # evaluation is run every 2000 iterations
   eval_batch_step: [0, 2000]
   # if pretrained_model is saved in static mode, load_static_weights must set to True
   cal_metric_during_train: True
@@ -13,7 +13,7 @@ Global:
   checkpoints:
   save_inference_dir:
   use_visualdl: False
-  infer_img: doc/imgs_words/ch/word_1.jpg
+  infer_img: doc/imgs_words_en/word_10.png
   # for data or label process
   character_dict_path: 
   character_type: en
@@ -21,7 +21,6 @@ Global:
   infer_mode: False
   use_space_char: False
 
-
 Optimizer:
   name: Adam
   beta1: 0.9
@@ -71,7 +70,7 @@ Train:
       - KeepKeys:
           keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
   loader:
-    shuffle: False
+    shuffle: True
     batch_size_per_card: 256
     drop_last: True
     num_workers: 8

configs/rec/rec_r34_vd_none_none_ctc.yml

+Global:
+  use_gpu: true
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/r34_vd_none_none_ctc/
+  save_epoch_step: 3
+  # evaluation is run every 2000 iterations
+  eval_batch_step: [0, 2000]
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path:
+  character_type: en
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0005
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+Architecture:
+  model_type: rec
+  algorithm: Rosetta
+  Backbone:
+    name: ResNet
+    layers: 34
+  Neck:
+    name: SequenceEncoder
+    encoder_type: reshape
+  Head:
+    name: CTCHead
+    fc_decay: 0.0004
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: LMDBDateSet
+    data_dir: ./train_data/data_lmdb_release/training/
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: LMDBDateSet
+    data_dir: ./train_data/data_lmdb_release/validation/
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 4

configs/rec/rec_r34_vd_tps_bilstm_ctc.yml

@@ -5,7 +5,7 @@ Global:
   print_batch_step: 10
   save_model_dir: ./output/rec/r34_vd_tps_bilstm_ctc/
   save_epoch_step: 3
-  # evaluation is run every 5000 iterations after the 4000th iteration
+  # evaluation is run every 2000 iterations
   eval_batch_step: [0, 2000]
   # if pretrained_model is saved in static mode, load_static_weights must set to True
   cal_metric_during_train: True
@@ -13,7 +13,7 @@ Global:
   checkpoints:
   save_inference_dir:
   use_visualdl: False
-  infer_img: doc/imgs_words/ch/word_1.jpg
+  infer_img: doc/imgs_words_en/word_10.png
   # for data or label process
   character_dict_path: 
   character_type: en
@@ -21,7 +21,6 @@ Global:
   infer_mode: False
   use_space_char: False
 
-
 Optimizer:
   name: Adam
   beta1: 0.9
@@ -34,7 +33,7 @@ Optimizer:
 
 Architecture:
   model_type: rec
-  algorithm: CRNN
+  algorithm: STARNet
   Transform:
     name: TPS
     num_fiducial: 20

ppocr/utils/character.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import string
-import re
-from .check import check_config_params
-import sys
-
-
-class CharacterOps(object):
-    """ Convert between text-label and text-index """
-
-    def __init__(self, config):
-        self.character_type = config['character_type']
-        self.loss_type = config['loss_type']
-        self.max_text_len = config['max_text_length']
-        if self.character_type == "en":
-            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
-            dict_character = list(self.character_str)
-        elif self.character_type == "ch":
-            character_dict_path = config['character_dict_path']
-            add_space = False
-            if 'use_space_char' in config:
-                add_space = config['use_space_char']
-            self.character_str = ""
-            with open(character_dict_path, "rb") as fin:
-                lines = fin.readlines()
-                for line in lines:
-                    line = line.decode('utf-8').strip("\n").strip("\r\n")
-                    self.character_str += line
-            if add_space:
-                self.character_str += " "
-            dict_character = list(self.character_str)
-        elif self.character_type == "en_sensitive":
-            # same with ASTER setting (use 94 char).
-            self.character_str = string.printable[:-6]
-            dict_character = list(self.character_str)
-        else:
-            self.character_str = None
-        assert self.character_str is not None, \
-            "Nonsupport type of the character: {}".format(self.character_str)
-        self.beg_str = "sos"
-        self.end_str = "eos"
-        if self.loss_type == "attention":
-            dict_character = [self.beg_str, self.end_str] + dict_character
-        elif self.loss_type == "srn":
-            dict_character = dict_character + [self.beg_str, self.end_str]
-        self.dict = {}
-        for i, char in enumerate(dict_character):
-            self.dict[char] = i
-        self.character = dict_character
-
-    def encode(self, text):
-        """convert text-label into text-index.
-        input:
-            text: text labels of each image. [batch_size]
-
-        output:
-            text: concatenated text index for CTCLoss.
-                    [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
-            length: length of each text. [batch_size]
-        """
-        if self.character_type == "en":
-            text = text.lower()
-
-        text_list = []
-        for char in text:
-            if char not in self.dict:
-                continue
-            text_list.append(self.dict[char])
-        text = np.array(text_list)
-        return text
-
-    def decode(self, text_index, is_remove_duplicate=False):
-        """ convert text-index into text-label. """
-        char_list = []
-        char_num = self.get_char_num()
-
-        if self.loss_type == "attention":
-            beg_idx = self.get_beg_end_flag_idx("beg")
-            end_idx = self.get_beg_end_flag_idx("end")
-            ignored_tokens = [beg_idx, end_idx]
-        else:
-            ignored_tokens = [char_num]
-
-        for idx in range(len(text_index)):
-            if text_index[idx] in ignored_tokens:
-                continue
-            if is_remove_duplicate:
-                if idx > 0 and text_index[idx - 1] == text_index[idx]:
-                    continue
-            char_list.append(self.character[int(text_index[idx])])
-        text = ''.join(char_list)
-        return text
-
-    def get_char_num(self):
-        return len(self.character)
-
-    def get_beg_end_flag_idx(self, beg_or_end):
-        if self.loss_type == "attention":
-            if beg_or_end == "beg":
-                idx = np.array(self.dict[self.beg_str])
-            elif beg_or_end == "end":
-                idx = np.array(self.dict[self.end_str])
-            else:
-                assert False, "Unsupport type %s in get_beg_end_flag_idx"\
-                    % beg_or_end
-            return idx
-        else:
-            err = "error in get_beg_end_flag_idx when using the loss %s"\
-                % (self.loss_type)
-            assert False, err
-
-
-def cal_predicts_accuracy(char_ops,
-                          preds,
-                          preds_lod,
-                          labels,
-                          labels_lod,
-                          is_remove_duplicate=False):
-    acc_num = 0
-    img_num = 0
-    for ino in range(len(labels_lod) - 1):
-        beg_no = preds_lod[ino]
-        end_no = preds_lod[ino + 1]
-        preds_text = preds[beg_no:end_no].reshape(-1)
-        preds_text = char_ops.decode(preds_text, is_remove_duplicate)
-
-        beg_no = labels_lod[ino]
-        end_no = labels_lod[ino + 1]
-        labels_text = labels[beg_no:end_no].reshape(-1)
-        labels_text = char_ops.decode(labels_text, is_remove_duplicate)
-        img_num += 1
-
-        if preds_text == labels_text:
-            acc_num += 1
-    acc = acc_num * 1.0 / img_num
-    return acc, acc_num, img_num
-
-
-def cal_predicts_accuracy_srn(char_ops,
-                              preds,
-                              labels,
-                              max_text_len,
-                              is_debug=False):
-    acc_num = 0
-    img_num = 0
-
-    char_num = char_ops.get_char_num()
-
-    total_len = preds.shape[0]
-    img_num = int(total_len / max_text_len)
-    for i in range(img_num):
-        cur_label = []
-        cur_pred = []
-        for j in range(max_text_len):
-            if labels[j + i * max_text_len] != int(char_num-1):  #0
-                cur_label.append(labels[j + i * max_text_len][0])
-            else:
-                break
-
-        for j in range(max_text_len + 1):
-            if j < len(cur_label) and preds[j + i * max_text_len][
-                    0] != cur_label[j]:
-                break
-            elif j == len(cur_label) and j == max_text_len:
-                acc_num += 1
-                break
-            elif j == len(cur_label) and preds[j + i * max_text_len][0] == int(char_num-1):
-                acc_num += 1
-                break
-    acc = acc_num * 1.0 / img_num
-    return acc, acc_num, img_num
-
-
-def convert_rec_attention_infer_res(preds):
-    img_num = preds.shape[0]
-    target_lod = [0]
-    convert_ids = []
-    for ino in range(img_num):
-        end_pos = np.where(preds[ino, :] == 1)[0]
-        if len(end_pos) <= 1:
-            text_list = preds[ino, 1:]
-        else:
-            text_list = preds[ino, 1:end_pos[1]]
-        target_lod.append(target_lod[ino] + len(text_list))
-        convert_ids = convert_ids + list(text_list)
-    convert_ids = np.array(convert_ids)
-    convert_ids = convert_ids.reshape((-1, 1))
-    return convert_ids, target_lod
-
-
-def convert_rec_label_to_lod(ori_labels):
-    img_num = len(ori_labels)
-    target_lod = [0]
-    convert_ids = []
-    for ino in range(img_num):
-        target_lod.append(target_lod[ino] + len(ori_labels[ino]))
-        convert_ids = convert_ids + list(ori_labels[ino])
-    convert_ids = np.array(convert_ids)
-    convert_ids = convert_ids.reshape((-1, 1))
-    return convert_ids, target_lod

ppocr/utils/check.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
-import sys
-
-import logging
-logger = logging.getLogger(__name__)
-
-
-def check_config_params(config, config_name, params):
-    for param in params:
-        if param not in config:
-            err = "param %s didn't find in %s!" % (param, config_name)
-            assert False, err
-    return